import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer
class SCREAMING_SNAKE_CASE_		:







   def __init__(     self						:						Dict   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						str=13   ,   lowerCamelCase_						:						List[Any]=7   ,   lowerCamelCase_						:						List[Any]=True   ,   lowerCamelCase_						:						Union[str, Any]=True   ,   lowerCamelCase_						:						Union[str, Any]=False   ,   lowerCamelCase_						:						List[str]=True   ,   lowerCamelCase_						:						int=99   ,   lowerCamelCase_						:						Dict=32   ,   lowerCamelCase_						:						Optional[Any]=5   ,   lowerCamelCase_						:						int=4   ,   lowerCamelCase_						:						int=37   ,   lowerCamelCase_						:						Dict="gelu"   ,   lowerCamelCase_						:						Dict=0.1   ,   lowerCamelCase_						:						str=0.1   ,   lowerCamelCase_						:						int=512   ,   lowerCamelCase_						:						List[str]=16   ,   lowerCamelCase_						:						Tuple=2   ,   lowerCamelCase_						:						int=0.0_2   ,   lowerCamelCase_						:						Union[str, Any]=3   ,   lowerCamelCase_						:						int=4   ,   lowerCamelCase_						:						str=None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      parent
    UpperCamelCase			  =      batch_size
    UpperCamelCase			  =      seq_length
    UpperCamelCase			  =      is_training
    UpperCamelCase			  =      use_input_mask
    UpperCamelCase			  =      use_token_type_ids
    UpperCamelCase			  =      use_labels
    UpperCamelCase			  =      vocab_size
    UpperCamelCase			  =      hidden_size
    UpperCamelCase			  =      num_hidden_layers
    UpperCamelCase			  =      num_attention_heads
    UpperCamelCase			  =      intermediate_size
    UpperCamelCase			  =      hidden_act
    UpperCamelCase			  =      hidden_dropout_prob
    UpperCamelCase			  =      attention_probs_dropout_prob
    UpperCamelCase			  =      max_position_embeddings
    UpperCamelCase			  =      type_vocab_size
    UpperCamelCase			  =      type_sequence_label_size
    UpperCamelCase			  =      initializer_range
    UpperCamelCase			  =      num_labels
    UpperCamelCase			  =      num_choices
    UpperCamelCase			  =      scope







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      ids_tensor([self.batch_size, self.seq_length]   ,   self.vocab_size				)

    UpperCamelCase			  =      None
    if self.use_input_mask:
     UpperCamelCase			  =      random_attention_mask([self.batch_size, self.seq_length]				)

    UpperCamelCase			  =      None
    if self.use_token_type_ids:
     UpperCamelCase			  =      ids_tensor([self.batch_size, self.seq_length]   ,   self.type_vocab_size				)

    UpperCamelCase			  =      None
    UpperCamelCase			  =      None
    UpperCamelCase			  =      None
    if self.use_labels:
     UpperCamelCase			  =      ids_tensor([self.batch_size]   ,   self.type_sequence_label_size				)
     UpperCamelCase			  =      ids_tensor([self.batch_size, self.seq_length]   ,   self.num_labels				)
     UpperCamelCase			  =      ids_tensor([self.batch_size]   ,   self.num_choices				)

    UpperCamelCase			  =      self.get_config()

    return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    return LlamaConfig(
        vocab_size=self.vocab_size   ,   hidden_size=self.hidden_size   ,   num_hidden_layers=self.num_hidden_layers   ,   num_attention_heads=self.num_attention_heads   ,   intermediate_size=self.intermediate_size   ,   hidden_act=self.hidden_act   ,   hidden_dropout_prob=self.hidden_dropout_prob   ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,   max_position_embeddings=self.max_position_embeddings   ,   type_vocab_size=self.type_vocab_size   ,   is_decoder=lowerCamelCase_   ,   initializer_range=self.initializer_range   ,   )







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						Dict   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Dict   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      LlamaModel(config=lowerCamelCase_				)
    model.to(lowerCamelCase_				)
    model.eval()
    UpperCamelCase			  =      model(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				)
    UpperCamelCase			  =      model(lowerCamelCase_				)
    self.parent.assertEqual(result.last_hidden_state.shape   ,   (self.batch_size, self.seq_length, self.hidden_size)				)







   def 						lowerCamelCase_	(     self						:						Any   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						Optional[int]   ,   lowerCamelCase_						:						Union[str, Any]   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Dict   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						int   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      True
    UpperCamelCase			  =      LlamaModel(lowerCamelCase_				)
    model.to(lowerCamelCase_				)
    model.eval()
    UpperCamelCase			  =      model(
        lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   encoder_hidden_states=lowerCamelCase_   ,   encoder_attention_mask=lowerCamelCase_   ,   )
    UpperCamelCase			  =      model(
        lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   encoder_hidden_states=lowerCamelCase_   ,   )
    UpperCamelCase			  =      model(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				)
    self.parent.assertEqual(result.last_hidden_state.shape   ,   (self.batch_size, self.seq_length, self.hidden_size)				)







   def 						lowerCamelCase_	(     self						:						List[str]   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Optional[int]   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Optional[int]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Optional[int]   ,   lowerCamelCase_						:						Optional[Any]   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      LlamaForCausalLM(config=lowerCamelCase_				)
    model.to(lowerCamelCase_				)
    model.eval()
    UpperCamelCase			  =      model(lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   labels=lowerCamelCase_				)
    self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.seq_length, self.vocab_size)				)







   def 						lowerCamelCase_	(     self						:						Optional[int]   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						Union[str, Any]   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Tuple   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      True
    UpperCamelCase			  =      True
    UpperCamelCase			  =      LlamaForCausalLM(config=lowerCamelCase_				)
    model.to(lowerCamelCase_				)
    model.eval()

    # first forward pass
    UpperCamelCase			  =      model(
        lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   encoder_hidden_states=lowerCamelCase_   ,   encoder_attention_mask=lowerCamelCase_   ,   use_cache=lowerCamelCase_   ,   )
    UpperCamelCase			  =      outputs.past_key_values

    # create hypothetical multiple next token and extent to next_input_ids
    UpperCamelCase			  =      ids_tensor((self.batch_size, 3)   ,   config.vocab_size				)
    UpperCamelCase			  =      ids_tensor((self.batch_size, 3)   ,   vocab_size=2				)

    # append to next input_ids and
    UpperCamelCase			  =      torch.cat([input_ids, next_tokens]   ,   dim=-1				)
    UpperCamelCase			  =      torch.cat([input_mask, next_mask]   ,   dim=-1				)

    UpperCamelCase			  =      model(
        lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   encoder_hidden_states=lowerCamelCase_   ,   encoder_attention_mask=lowerCamelCase_   ,   output_hidden_states=lowerCamelCase_   ,   )["""hidden_states"""][0]
    UpperCamelCase			  =      model(
        lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   encoder_hidden_states=lowerCamelCase_   ,   encoder_attention_mask=lowerCamelCase_   ,   past_key_values=lowerCamelCase_   ,   output_hidden_states=lowerCamelCase_   ,   )["""hidden_states"""][0]

    # select random slice
    UpperCamelCase			  =      ids_tensor((1,)   ,   output_from_past.shape[-1]				).item()
    UpperCamelCase			  =      output_from_no_past[:, -3:, random_slice_idx].detach()
    UpperCamelCase			  =      output_from_past[:, :, random_slice_idx].detach()

    self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]				)

    # test that outputs are equal for slice
    self.parent.assertTrue(torch.allclose(lowerCamelCase_   ,   lowerCamelCase_   ,   atol=1E-3				)				)







   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.prepare_config_and_inputs()
    (
        (
        UpperCamelCase
    )  ,     (
        UpperCamelCase
    )  ,     (
        UpperCamelCase
    )  ,     (
        UpperCamelCase
    )  ,     (
        UpperCamelCase
    )  ,     (
        UpperCamelCase
    )  ,     (
        UpperCamelCase
    )  ,     
    )			  =      config_and_inputs
    UpperCamelCase			  =      {"""input_ids""": input_ids, """attention_mask""": input_mask}
    return config, inputs_dict





@require_torch
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     __lowerCAmelCase  ,     __lowerCAmelCase  ,     unittest.TestCase		):
   __lowerCAmelCase           =				(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
   __lowerCAmelCase           =				(LlamaForCausalLM,) if is_torch_available() else ()
   __lowerCAmelCase           =				(
       {
           """feature-extraction""": LlamaModel,
           """text-classification""": LlamaForSequenceClassification,
           """text-generation""": LlamaForCausalLM,
           """zero-shot""": LlamaForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   __lowerCAmelCase           =				False
   __lowerCAmelCase           =				False







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      LlamaModelTester(self				)
    UpperCamelCase			  =      ConfigTester(self   ,   config_class=lowerCamelCase_   ,   hidden_size=37				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    self.config_tester.run_common_tests()







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs()
    for type in ["absolute", "relative_key", "relative_key_query"]:
     UpperCamelCase			  =      type
     self.model_tester.create_and_check_model(*lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs_for_common()
    UpperCamelCase			  =      3
    UpperCamelCase			  =      input_dict["""input_ids"""]
    UpperCamelCase			  =      input_ids.ne(1				).to(lowerCamelCase_				)
    UpperCamelCase			  =      ids_tensor([self.model_tester.batch_size]   ,   self.model_tester.type_sequence_label_size				)
    UpperCamelCase			  =      LlamaForSequenceClassification(lowerCamelCase_				)
    model.to(lowerCamelCase_				)
    model.eval()
    UpperCamelCase			  =      model(lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   labels=lowerCamelCase_				)
    self.assertEqual(result.logits.shape   ,   (self.model_tester.batch_size, self.model_tester.num_labels)				)







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs_for_common()
    UpperCamelCase			  =      3
    UpperCamelCase			  =      """single_label_classification"""
    UpperCamelCase			  =      input_dict["""input_ids"""]
    UpperCamelCase			  =      input_ids.ne(1				).to(lowerCamelCase_				)
    UpperCamelCase			  =      ids_tensor([self.model_tester.batch_size]   ,   self.model_tester.type_sequence_label_size				)
    UpperCamelCase			  =      LlamaForSequenceClassification(lowerCamelCase_				)
    model.to(lowerCamelCase_				)
    model.eval()
    UpperCamelCase			  =      model(lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   labels=lowerCamelCase_				)
    self.assertEqual(result.logits.shape   ,   (self.model_tester.batch_size, self.model_tester.num_labels)				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs_for_common()
    UpperCamelCase			  =      3
    UpperCamelCase			  =      """multi_label_classification"""
    UpperCamelCase			  =      input_dict["""input_ids"""]
    UpperCamelCase			  =      input_ids.ne(1				).to(lowerCamelCase_				)
    UpperCamelCase			  =      ids_tensor(
        [self.model_tester.batch_size, config.num_labels]   ,   self.model_tester.type_sequence_label_size				).to(torch.float				)
    UpperCamelCase			  =      LlamaForSequenceClassification(lowerCamelCase_				)
    model.to(lowerCamelCase_				)
    model.eval()
    UpperCamelCase			  =      model(lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   labels=lowerCamelCase_				)
    self.assertEqual(result.logits.shape   ,   (self.model_tester.batch_size, self.model_tester.num_labels)				)







   @unittest.skip("""LLaMA buffers include complex numbers, which breaks this test"""				)
   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    pass







   @parameterized.expand([("""linear""",), ("""dynamic""",)]				)
   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs_for_common()
    UpperCamelCase			  =      ids_tensor([1, 10]   ,   config.vocab_size				)
    UpperCamelCase			  =      ids_tensor([1, int(config.max_position_embeddings * 1.5				)]   ,   config.vocab_size				)

    set_seed(42				)  # Fixed seed at init time so the two models get the same random weights
    UpperCamelCase			  =      LlamaModel(lowerCamelCase_				)
    original_model.to(lowerCamelCase_				)
    original_model.eval()
    UpperCamelCase			  =      original_model(lowerCamelCase_				).last_hidden_state
    UpperCamelCase			  =      original_model(lowerCamelCase_				).last_hidden_state

    set_seed(42				)  # Fixed seed at init time so the two models get the same random weights
    UpperCamelCase			  =      {"""type""": scaling_type, """factor""": 1_0.0}
    UpperCamelCase			  =      LlamaModel(lowerCamelCase_				)
    scaled_model.to(lowerCamelCase_				)
    scaled_model.eval()
    UpperCamelCase			  =      scaled_model(lowerCamelCase_				).last_hidden_state
    UpperCamelCase			  =      scaled_model(lowerCamelCase_				).last_hidden_state

    # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
    # maximum sequence length, so the outputs for the short input should match.
    if scaling_type == "dynamic":
     self.assertTrue(torch.allclose(lowerCamelCase_   ,   lowerCamelCase_   ,   atol=1E-5				)				)
    else:
     self.assertFalse(torch.allclose(lowerCamelCase_   ,   lowerCamelCase_   ,   atol=1E-5				)				)

    # The output should be different for long inputs
    self.assertFalse(torch.allclose(lowerCamelCase_   ,   lowerCamelCase_   ,   atol=1E-5				)				)





@require_torch
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""				)
   @slow
   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      [1, 306, 4658, 278, 6593, 310, 2834, 338]
    UpperCamelCase			  =      LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-7b-hf"""   ,   device_map="""auto"""				)
    UpperCamelCase			  =      model(torch.tensor([input_ids]				)				)
    # Expected mean on dim = -1
    UpperCamelCase			  =      torch.tensor([[-6.6_5_5_0, -4.1_2_2_7, -4.9_8_5_9, -3.2_4_0_6, 0.8_2_6_2, -3.0_0_3_3, 1.2_9_6_4, -3.3_6_9_9]]				)
    torch.testing.assert_close(out.mean(-1				)   ,   lowerCamelCase_   ,   atol=1E-2   ,   rtol=1E-2				)
    # slicing logits[0, 0, 0:30]
    # fmt: off
    UpperCamelCase			  =      torch.tensor([-1_2.8_2_8_1, -7.4_4_5_3, -0.4_6_3_9, -8.0_6_2_5, -7.2_5_0_0, -8.0_0_0_0, -6.4_8_8_3, -7.7_6_9_5, -7.8_4_3_8, -7.0_3_1_2, -6.2_1_8_8, -7.1_3_2_8, -1.8_4_9_6, 1.9_9_6_1, -8.6_2_5_0, -6.7_2_2_7, -1_2.8_2_8_1, -6.9_4_9_2, -7.0_7_4_2, -7.7_8_5_2, -7.5_8_2_0, -7.9_0_6_2, -6.9_3_7_5, -7.9_8_0_5, -8.3_4_3_8, -8.1_5_6_2, -8.0_4_6_9, -7.6_2_5_0, -7.7_4_2_2, -7.3_3_9_8,]				)
    # fmt: on
    torch.testing.assert_close(out[0, 0, :30]   ,   lowerCamelCase_   ,   atol=1E-5   ,   rtol=1E-5				)







   @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""				)
   @slow
   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      [1, 306, 4658, 278, 6593, 310, 2834, 338]
    UpperCamelCase			  =      LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-hf"""   ,   device_map="""auto"""				)
    UpperCamelCase			  =      model(torch.tensor(lowerCamelCase_				)				)
    # Expected mean on dim = -1
    UpperCamelCase			  =      torch.tensor([[-2.0_6_2_2, -1.2_7_9_4, -1.1_6_3_8, -0.9_7_8_8, -1.4_6_0_3, -1.0_2_3_8, -1.7_8_9_3, -1.4_4_1_1]]				)
    torch.testing.assert_close(out.mean(-1				)   ,   lowerCamelCase_   ,   atol=1E-2   ,   rtol=1E-2				)
    # slicing logits[0, 0, 0:30]
    # fmt: off
    UpperCamelCase			  =      torch.tensor([-8.1_4_0_6, -8.0_5_4_7, 2.7_4_6_1, -1.2_3_4_4, -0.1_4_4_8, -1.8_2_6_2, -1.0_0_2_0, -1.8_1_5_4, -1.6_8_9_5, -1.8_5_1_6, -2.3_5_7_4, -0.9_2_7_7, 3.7_5_9_8, 6.5_7_4_2, -1.2_9_9_8, -0.1_1_7_7, -8.1_4_0_6, -2.9_6_8_8, -2.9_1_9_9, -3.1_6_9_9, -3.5_2_5_4, -2.3_5_5_5, -2.7_9_8_8, -3.4_1_4_1, -2.8_2_6_2, -4.5_1_9_5, -3.3_3_7_9, -3.3_1_6_4, -2.7_8_3_2, -3.0_2_7_3]				)
    # fmt: on
    torch.testing.assert_close(out[0, 0, :30]   ,   lowerCamelCase_   ,   atol=1E-5   ,   rtol=1E-5				)







   @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""				)
   @slow
   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      [1, 306, 4658, 278, 6593, 310, 2834, 338]
    UpperCamelCase			  =      LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""   ,   device_map="""auto"""				)
    UpperCamelCase			  =      model(torch.tensor(lowerCamelCase_				)				)
    # Expected mean on dim = -1
    UpperCamelCase			  =      torch.tensor([[-0.8_5_6_2, -1.8_5_2_0, -0.7_5_5_1, -0.4_1_6_2, -1.5_1_6_1, -1.2_0_3_8, -2.4_8_2_3, -2.3_2_5_4]]				)
    torch.testing.assert_close(out.mean(-1				)   ,   lowerCamelCase_   ,   atol=1E-2   ,   rtol=1E-2				)
    # slicing logits[0, 0, 0:30]
    # fmt: off
    UpperCamelCase			  =      torch.tensor([-2.2_2_2_7, 4.8_8_2_8, 0.9_0_2_3, -0.4_5_7_8, -0.7_8_7_1, -0.1_0_3_3, -0.6_2_2_1, -0.5_7_8_6, -0.7_8_0_3, -1.0_6_7_4, -1.2_9_2_0, -0.1_5_7_0, 0.8_0_0_8, 2.0_7_2_3, -0.9_4_9_7, 0.2_7_7_1, -2.2_2_2_7, -0.7_6_1_2, -1.4_3_4_6, -1.2_0_6_1, -1.6_4_2_6, -0.3_0_0_0, -0.7_1_3_9, -1.1_9_3_4, -1.8_6_9_1, -1.6_9_7_3, -1.5_9_4_7, -1.2_7_0_5, -0.3_5_2_3, -0.5_5_1_3]				)
    # fmt: on
    torch.testing.assert_close(out.mean(-1				)   ,   lowerCamelCase_   ,   atol=1E-2   ,   rtol=1E-2				)







   @unittest.skip(
       """Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test"""				)
   @slow
   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      [1, 306, 4658, 278, 6593, 310, 2834, 338]
    UpperCamelCase			  =      LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-70b-hf"""   ,   device_map="""auto"""				)
    UpperCamelCase			  =      model(torch.tensor(lowerCamelCase_				)				)

    UpperCamelCase			  =      torch.tensor(
        [[-4.2_3_2_7, -3.3_3_6_0, -4.6_6_6_5, -4.7_6_3_1, -1.8_1_8_0, -3.4_1_7_0, -1.4_2_1_1, -3.1_8_1_0]]   ,   dtype=torch.floataa				)
    torch.testing.assert_close(out.mean(-1				)   ,   lowerCamelCase_   ,   atol=1E-2   ,   rtol=1E-2				)
    # fmt: off
    UpperCamelCase			  =      torch.tensor([-9.4_9_2_2, -3.9_5_5_1, 1.7_9_9_8, -5.6_7_5_8, -5.1_0_5_5, -5.8_9_8_4, -4.8_3_2_0, -6.8_0_8_6, -6.5_3_9_1, -5.6_1_7_2, -5.5_8_2_0, -5.5_3_5_2, 1.7_8_8_1, 3.6_2_8_9, -6.5_1_1_7, -3.4_7_8_5, -9.5_0_0_0, -6.0_3_5_2, -6.8_1_2_5, -6.0_1_9_5, -6.6_8_3_6, -5.4_7_2_7, -6.2_8_1_2, -6.0_3_9_1, -7.3_3_9_8, -7.4_2_9_7, -7.4_8_4_4, -6.5_8_2_0, -5.8_7_8_9, -5.5_3_1_2]				)
    # fmt: on
    torch.testing.assert_close(out[0, 0, :30]   ,   lowerCamelCase_   ,   atol=1E-5   ,   rtol=1E-5				)







   @unittest.skip("""Model is curently gated"""				)
   @slow
   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      """Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the \"princi"""
    UpperCamelCase			  =      """Simply put, the theory of relativity states that """
    UpperCamelCase			  =      LlamaTokenizer.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""				)
    UpperCamelCase			  =      tokenizer.encode(lowerCamelCase_   ,   return_tensors="""pt"""				)
    UpperCamelCase			  =      LlamaForCausalLM.from_pretrained(
        """meta-llama/Llama-2-13b-chat-hf"""   ,   device_map="""sequential"""   ,   use_safetensors=lowerCamelCase_				)

    # greedy generation outputs
    UpperCamelCase			  =      model.generate(lowerCamelCase_   ,   max_new_tokens=64   ,   top_p=lowerCamelCase_   ,   temperature=1   ,   do_sample=lowerCamelCase_				)
    UpperCamelCase			  =      tokenizer.decode(generated_ids[0]   ,   skip_special_tokens=lowerCamelCase_				)
    self.assertEqual(lowerCamelCase_   ,   lowerCamelCase_				)

import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				["""image_processor""", """tokenizer"""]
   __lowerCAmelCase           =				"""LayoutLMv3ImageProcessor"""
   __lowerCAmelCase           =				("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")







   def __init__(     self						:						Dict   ,   lowerCamelCase_						:						Union[str, Any]=None   ,   lowerCamelCase_						:						Dict=None   ,   **lowerCamelCase_						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      None
    if "feature_extractor" in kwargs:
     warnings.warn(
         """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
         """ instead."""   ,   lowerCamelCase_   ,   )
     UpperCamelCase			  =      kwargs.pop("""feature_extractor"""				)

    UpperCamelCase			  =      image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
     raise ValueError("""You need to specify an `image_processor`."""				)
    if tokenizer is None:
     raise ValueError("""You need to specify a `tokenizer`."""				)

    super().__init__(lowerCamelCase_   ,   lowerCamelCase_				)







   def __call__(     self						:						int   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,   lowerCamelCase_						:						Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None   ,   lowerCamelCase_						:						Union[List[List[int]], List[List[List[int]]]] = None   ,   lowerCamelCase_						:						Optional[Union[List[int], List[List[int]]]] = None   ,   lowerCamelCase_						:						bool = True   ,   lowerCamelCase_						:						Union[bool, str, PaddingStrategy] = False   ,   lowerCamelCase_						:						Union[bool, str, TruncationStrategy] = None   ,   lowerCamelCase_						:						Optional[int] = None   ,   lowerCamelCase_						:						int = 0   ,   lowerCamelCase_						:						Optional[int] = None   ,   lowerCamelCase_						:						Optional[bool] = None   ,   lowerCamelCase_						:						Optional[bool] = None   ,   lowerCamelCase_						:						bool = False   ,   lowerCamelCase_						:						bool = False   ,   lowerCamelCase_						:						bool = False   ,   lowerCamelCase_						:						bool = False   ,   lowerCamelCase_						:						bool = True   ,   lowerCamelCase_						:						Optional[Union[str, TensorType]] = None   ,   **lowerCamelCase_						:						Optional[int]   ,   ):





    """simple docstring"""


    if self.image_processor.apply_ocr and (boxes is not None):
     raise ValueError(
         """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."""				)

    if self.image_processor.apply_ocr and (word_labels is not None):
     raise ValueError(
         """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""				)

    # first, apply the image processor
    UpperCamelCase			  =      self.image_processor(images=lowerCamelCase_   ,   return_tensors=lowerCamelCase_				)

    # second, apply the tokenizer
    if text is not None and self.image_processor.apply_ocr and text_pair is None:
     if isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
      UpperCamelCase			  =      [text]  # add batch dimension (as the image processor always adds a batch dimension)
     UpperCamelCase			  =      features["""words"""]

    UpperCamelCase			  =      self.tokenizer(
        text=text if text is not None else features["""words"""]   ,   text_pair=text_pair if text_pair is not None else None   ,   boxes=boxes if boxes is not None else features["""boxes"""]   ,   word_labels=lowerCamelCase_   ,   add_special_tokens=lowerCamelCase_   ,   padding=lowerCamelCase_   ,   truncation=lowerCamelCase_   ,   max_length=lowerCamelCase_   ,   stride=lowerCamelCase_   ,   pad_to_multiple_of=lowerCamelCase_   ,   return_token_type_ids=lowerCamelCase_   ,   return_attention_mask=lowerCamelCase_   ,   return_overflowing_tokens=lowerCamelCase_   ,   return_special_tokens_mask=lowerCamelCase_   ,   return_offsets_mapping=lowerCamelCase_   ,   return_length=lowerCamelCase_   ,   verbose=lowerCamelCase_   ,   return_tensors=lowerCamelCase_   ,   **lowerCamelCase_   ,   )

    # add pixel values
    UpperCamelCase			  =      features.pop("""pixel_values"""				)
    if return_overflowing_tokens is True:
     UpperCamelCase			  =      self.get_overflowing_images(lowerCamelCase_   ,   encoded_inputs["""overflow_to_sample_mapping"""]				)
    UpperCamelCase			  =      images

    return encoded_inputs







   def 						lowerCamelCase_	(     self						:						str   ,   lowerCamelCase_						:						Union[str, Any]   ,   lowerCamelCase_						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      []
    for sample_idx in overflow_to_sample_mapping:
     images_with_overflow.append(images[sample_idx]				)

    if len(lowerCamelCase_				) != len(lowerCamelCase_				):
     raise ValueError(
         """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
         f""" {len(lowerCamelCase_				)} and {len(lowerCamelCase_				)}"""				)

    return images_with_overflow







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   *lowerCamelCase_						:						Any   ,   **lowerCamelCase_						:						Union[str, Any]				):





    """simple docstring"""


    return self.tokenizer.batch_decode(*lowerCamelCase_   ,   **lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   *lowerCamelCase_						:						Dict   ,   **lowerCamelCase_						:						str				):





    """simple docstring"""


    return self.tokenizer.decode(*lowerCamelCase_   ,   **lowerCamelCase_				)







   @property
   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    return ["input_ids", "bbox", "attention_mask", "pixel_values"]







   @property
   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    warnings.warn(
        """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""   ,   lowerCamelCase_   ,   )
    return self.image_processor_class







   @property
   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    warnings.warn(
        """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""   ,   lowerCamelCase_   ,   )
    return self.image_processor
